The aim of this research is to create a database resource for images that will be used in analyses related to the detection and characterization of lung cancer using spiral CT. There has been significant interest in the last few years in using spiral CT lung scanning for lung cancer screening of patients at high risk. Early detection and intervention may significantly reduce the mortality rates of lung cancer and improve patient prognoses. In addition, there is significant interest in the characterization of solitary or small multiple nodules detected using lung cancer screening and conventional thoracic CT exams. This is because the presence of nodules within the lungs is not a reliable indicator of cancer. In fact, 50-80 percent of nodules detected by current methods are benign; this percentage may even climb as smaller nodules are detected with very sensitive screening techniques under consideration. Therefore, detection of suspicious objects in the lung parenchyma, while a very necessary step, is not sufficient for patient management. Additional imaging or processing of the CT images may provide information that is useful in establishing the diagnosis of the individual patient and determining the next step in patient management. However, research in this area has been limited by the difficulties in collecting cases on which image processing algorithms may be robustly developed and tested. This is because it is difficult to establish diagnostic truth for such key elements as lesion location and lesion diagnosis. The establishment of a lung imaging database creates a resource for the development and evaluation of methods for detecting and characterizing lung cancer. When made available to researchers all over the world, this resource would significantly reduce development time because it would allow imaging researchers to focus on the their areas of expertise without having to focus on case collection, establishing diagnostic truth and all of the other infrastructure issues that detract from development. This database would also allow direct and objective comparisons of techniques because common metrics would be applied to identical cases. This will allow the image processing field to move forward and to move from design to clinical implementation much faster. The specific aims to accomplish this are: SA-1 To develop the necessary consensus and standards for an image database resource related to the detection, characterization and evaluation of lung cancer using spiral CT imaging. SA-2 To construct, populate and test the database of spiral CT lung image data and ancillary data including the information necessary about diagnostic truth for each case. SA-3 To provide a means for documentation and distribution of this database to researchers through the internet.